ニューラルネット・遺伝的アルゴリズムを用いた粒子追跡画像計測法 Particle tracking velocimetry using neural networks and genetic algorithms

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Particle tracking velocimetry (PTV) is one of the methods to measure flow velocity fields which is considered to be essential and useful for analyzing complex flow fields. This paper proposes a temporally particle pairing method using genetic algorithms and a flow velocity field estimation method using neural networks. The particle pairing method is based on spatial pattern relationship between pair-candidate particles with their respective neighbour particles in two exposures taken over a small time interval. If two particles are paired correctly, the spatial patterns of their pair-candidate particles are to be similar. The method finds correct pairs by applying a genetic algorithm. A potential problem of the method is that it can't measure velocity vectors at the points where no particles exist. The flow velocity field estimation method proposed in this paper solves it, which uses neural networks. The neural network is trained by using measured velocity vectors as teaching data so that the derivatives of a certain scholar function agree well with the measured data. The continuity equation of flow is consequently satisfied in the estimated vector fields and the scholar function gives the stream function.

収録刊行物

  • 可視化情報学会誌 = Journal of the Visualization Society of Japan  

    可視化情報学会誌 = Journal of the Visualization Society of Japan 18, 59-60, 1998-09-01 

    The Visualization Society of Japan

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各種コード

  • NII論文ID(NAID)
    10002675859
  • NII書誌ID(NCID)
    AN10374478
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    09164731
  • NDL 記事登録ID
    4571905
  • NDL 雑誌分類
    ZM35(科学技術--物理学)
  • NDL 請求記号
    Z15-431
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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